Biostat 203B Homework 2

Due Feb 9 @ 11:59PM

Author

Jiachen Ai, UID:206182615

Display machine information for reproducibility:

sessionInfo()
R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.3

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/Los_Angeles
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] htmlwidgets_1.6.4 compiler_4.3.1    fastmap_1.1.1     cli_3.6.1        
 [5] tools_4.3.1       htmltools_0.5.7   rstudioapi_0.15.0 yaml_2.3.7       
 [9] rmarkdown_2.25    knitr_1.44        jsonlite_1.8.7    xfun_0.40        
[13] digest_0.6.33     rlang_1.1.1       evaluate_0.22    

Load necessary libraries (you can add more as needed).

library(arrow)
library(data.table)
library(memuse)
library(pryr)
library(R.utils)
library(tidyverse)

Display memory information of your computer

memuse::Sys.meminfo()
Totalram:   8.000 GiB 
Freeram:   68.406 MiB 

In this exercise, we explore various tools for ingesting the MIMIC-IV data introduced in homework 1.

Display the contents of MIMIC hosp and icu data folders:

ls -l ~/mimic/hosp/
total 8859752
-rw-rw-r--@ 1 jacenai  staff    15516088 Jan  5  2023 admissions.csv.gz
-rw-rw-r--@ 1 jacenai  staff      427468 Jan  5  2023 d_hcpcs.csv.gz
-rw-rw-r--@ 1 jacenai  staff      859438 Jan  5  2023 d_icd_diagnoses.csv.gz
-rw-rw-r--@ 1 jacenai  staff      578517 Jan  5  2023 d_icd_procedures.csv.gz
-rw-rw-r--@ 1 jacenai  staff       12900 Jan  5  2023 d_labitems.csv.gz
-rw-rw-r--@ 1 jacenai  staff    25070720 Jan  5  2023 diagnoses_icd.csv.gz
-rw-rw-r--@ 1 jacenai  staff     7426955 Jan  5  2023 drgcodes.csv.gz
-rw-rw-r--@ 1 jacenai  staff   508524623 Jan  5  2023 emar.csv.gz
-rw-rw-r--@ 1 jacenai  staff   471096030 Jan  5  2023 emar_detail.csv.gz
-rw-rw-r--@ 1 jacenai  staff     1767138 Jan  5  2023 hcpcsevents.csv.gz
-rw-rw-r--@ 1 jacenai  staff  1939088924 Jan  5  2023 labevents.csv.gz
-rw-rw-r--@ 1 jacenai  staff    96698496 Jan  5  2023 microbiologyevents.csv.gz
-rw-rw-r--@ 1 jacenai  staff    36124944 Jan  5  2023 omr.csv.gz
-rw-rw-r--@ 1 jacenai  staff     2312631 Jan  5  2023 patients.csv.gz
-rw-rw-r--@ 1 jacenai  staff   398753125 Jan  5  2023 pharmacy.csv.gz
-rw-rw-r--@ 1 jacenai  staff   498505135 Jan  5  2023 poe.csv.gz
-rw-rw-r--@ 1 jacenai  staff    25477219 Jan  5  2023 poe_detail.csv.gz
-rw-rw-r--@ 1 jacenai  staff   458817415 Jan  5  2023 prescriptions.csv.gz
-rw-rw-r--@ 1 jacenai  staff     6027067 Jan  5  2023 procedures_icd.csv.gz
-rw-rw-r--@ 1 jacenai  staff      122507 Jan  5  2023 provider.csv.gz
-rw-rw-r--@ 1 jacenai  staff     6781247 Jan  5  2023 services.csv.gz
-rw-rw-r--@ 1 jacenai  staff    36158338 Jan  5  2023 transfers.csv.gz
ls -l ~/mimic/icu/
total 6155968
-rw-rw-r--@ 1 jacenai  staff       35893 Jan  5  2023 caregiver.csv.gz
-rw-rw-r--@ 1 jacenai  staff  2467761053 Jan  5  2023 chartevents.csv.gz
-rw-rw-r--@ 1 jacenai  staff       57476 Jan  5  2023 d_items.csv.gz
-rw-rw-r--@ 1 jacenai  staff    45721062 Jan  5  2023 datetimeevents.csv.gz
-rw-rw-r--@ 1 jacenai  staff     2614571 Jan  5  2023 icustays.csv.gz
-rw-rw-r--@ 1 jacenai  staff   251962313 Jan  5  2023 ingredientevents.csv.gz
-rw-rw-r--@ 1 jacenai  staff   324218488 Jan  5  2023 inputevents.csv.gz
-rw-rw-r--@ 1 jacenai  staff    38747895 Jan  5  2023 outputevents.csv.gz
-rw-rw-r--@ 1 jacenai  staff    20717852 Jan  5  2023 procedureevents.csv.gz

Q1. read.csv (base R) vs read_csv (tidyverse) vs fread (data.table)

Q1.1 Speed, memory, and data types

There are quite a few utilities in R for reading plain text data files. Let us test the speed of reading a moderate sized compressed csv file, admissions.csv.gz, by three functions: read.csv in base R, read_csv in tidyverse, and fread in the data.table package.

Which function is fastest? Is there difference in the (default) parsed data types? How much memory does each resultant dataframe or tibble use? (Hint: system.time measures run times; pryr::object_size measures memory usage.)

Answer

library(tidyverse)
library(data.table)
library(pryr)

mimic_path <- "~/mimic/hosp/"
# reading time for read.csv
system.time(data_read.csv <- read.csv(
  str_c(mimic_path,"admissions.csv.gz")))
   user  system elapsed 
  4.788   0.081   4.879 
# reading time for read_csv
system.time(data_read_csv <- read_csv(
  str_c(mimic_path,"admissions.csv.gz")))
   user  system elapsed 
  1.429   0.097   0.837 
# reading time for fread
system.time(data_fread <- fread(
  str_c(mimic_path,"admissions.csv.gz")))
   user  system elapsed 
  0.731   0.051   0.794 
# memory usage for read.csv
pryr::object_size(data_read.csv)
158.71 MB
# memory usage for read_csv
pryr::object_size(data_read_csv)
55.31 MB
# memory usage for fread
pryr::object_size(data_fread)
50.13 MB
# data types for read.csv
str(data_read.csv)
'data.frame':   431231 obs. of  16 variables:
 $ subject_id          : int  10000032 10000032 10000032 10000032 10000068 10000084 10000084 10000108 10000117 10000117 ...
 $ hadm_id             : int  22595853 22841357 25742920 29079034 25022803 23052089 29888819 27250926 22927623 27988844 ...
 $ admittime           : chr  "2180-05-06 22:23:00" "2180-06-26 18:27:00" "2180-08-05 23:44:00" "2180-07-23 12:35:00" ...
 $ dischtime           : chr  "2180-05-07 17:15:00" "2180-06-27 18:49:00" "2180-08-07 17:50:00" "2180-07-25 17:55:00" ...
 $ deathtime           : chr  "" "" "" "" ...
 $ admission_type      : chr  "URGENT" "EW EMER." "EW EMER." "EW EMER." ...
 $ admit_provider_id   : chr  "P874LG" "P09Q6Y" "P60CC5" "P30KEH" ...
 $ admission_location  : chr  "TRANSFER FROM HOSPITAL" "EMERGENCY ROOM" "EMERGENCY ROOM" "EMERGENCY ROOM" ...
 $ discharge_location  : chr  "HOME" "HOME" "HOSPICE" "HOME" ...
 $ insurance           : chr  "Other" "Medicaid" "Medicaid" "Medicaid" ...
 $ language            : chr  "ENGLISH" "ENGLISH" "ENGLISH" "ENGLISH" ...
 $ marital_status      : chr  "WIDOWED" "WIDOWED" "WIDOWED" "WIDOWED" ...
 $ race                : chr  "WHITE" "WHITE" "WHITE" "WHITE" ...
 $ edregtime           : chr  "2180-05-06 19:17:00" "2180-06-26 15:54:00" "2180-08-05 20:58:00" "2180-07-23 05:54:00" ...
 $ edouttime           : chr  "2180-05-06 23:30:00" "2180-06-26 21:31:00" "2180-08-06 01:44:00" "2180-07-23 14:00:00" ...
 $ hospital_expire_flag: int  0 0 0 0 0 0 0 0 0 0 ...
# data types for read_csv
str(data_read_csv)
spc_tbl_ [431,231 × 16] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ subject_id          : num [1:431231] 1e+07 1e+07 1e+07 1e+07 1e+07 ...
 $ hadm_id             : num [1:431231] 22595853 22841357 25742920 29079034 25022803 ...
 $ admittime           : POSIXct[1:431231], format: "2180-05-06 22:23:00" "2180-06-26 18:27:00" ...
 $ dischtime           : POSIXct[1:431231], format: "2180-05-07 17:15:00" "2180-06-27 18:49:00" ...
 $ deathtime           : POSIXct[1:431231], format: NA NA ...
 $ admission_type      : chr [1:431231] "URGENT" "EW EMER." "EW EMER." "EW EMER." ...
 $ admit_provider_id   : chr [1:431231] "P874LG" "P09Q6Y" "P60CC5" "P30KEH" ...
 $ admission_location  : chr [1:431231] "TRANSFER FROM HOSPITAL" "EMERGENCY ROOM" "EMERGENCY ROOM" "EMERGENCY ROOM" ...
 $ discharge_location  : chr [1:431231] "HOME" "HOME" "HOSPICE" "HOME" ...
 $ insurance           : chr [1:431231] "Other" "Medicaid" "Medicaid" "Medicaid" ...
 $ language            : chr [1:431231] "ENGLISH" "ENGLISH" "ENGLISH" "ENGLISH" ...
 $ marital_status      : chr [1:431231] "WIDOWED" "WIDOWED" "WIDOWED" "WIDOWED" ...
 $ race                : chr [1:431231] "WHITE" "WHITE" "WHITE" "WHITE" ...
 $ edregtime           : POSIXct[1:431231], format: "2180-05-06 19:17:00" "2180-06-26 15:54:00" ...
 $ edouttime           : POSIXct[1:431231], format: "2180-05-06 23:30:00" "2180-06-26 21:31:00" ...
 $ hospital_expire_flag: num [1:431231] 0 0 0 0 0 0 0 0 0 0 ...
 - attr(*, "spec")=
  .. cols(
  ..   subject_id = col_double(),
  ..   hadm_id = col_double(),
  ..   admittime = col_datetime(format = ""),
  ..   dischtime = col_datetime(format = ""),
  ..   deathtime = col_datetime(format = ""),
  ..   admission_type = col_character(),
  ..   admit_provider_id = col_character(),
  ..   admission_location = col_character(),
  ..   discharge_location = col_character(),
  ..   insurance = col_character(),
  ..   language = col_character(),
  ..   marital_status = col_character(),
  ..   race = col_character(),
  ..   edregtime = col_datetime(format = ""),
  ..   edouttime = col_datetime(format = ""),
  ..   hospital_expire_flag = col_double()
  .. )
 - attr(*, "problems")=<externalptr> 
# data types for fread
str(data_read_csv)
spc_tbl_ [431,231 × 16] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ subject_id          : num [1:431231] 1e+07 1e+07 1e+07 1e+07 1e+07 ...
 $ hadm_id             : num [1:431231] 22595853 22841357 25742920 29079034 25022803 ...
 $ admittime           : POSIXct[1:431231], format: "2180-05-06 22:23:00" "2180-06-26 18:27:00" ...
 $ dischtime           : POSIXct[1:431231], format: "2180-05-07 17:15:00" "2180-06-27 18:49:00" ...
 $ deathtime           : POSIXct[1:431231], format: NA NA ...
 $ admission_type      : chr [1:431231] "URGENT" "EW EMER." "EW EMER." "EW EMER." ...
 $ admit_provider_id   : chr [1:431231] "P874LG" "P09Q6Y" "P60CC5" "P30KEH" ...
 $ admission_location  : chr [1:431231] "TRANSFER FROM HOSPITAL" "EMERGENCY ROOM" "EMERGENCY ROOM" "EMERGENCY ROOM" ...
 $ discharge_location  : chr [1:431231] "HOME" "HOME" "HOSPICE" "HOME" ...
 $ insurance           : chr [1:431231] "Other" "Medicaid" "Medicaid" "Medicaid" ...
 $ language            : chr [1:431231] "ENGLISH" "ENGLISH" "ENGLISH" "ENGLISH" ...
 $ marital_status      : chr [1:431231] "WIDOWED" "WIDOWED" "WIDOWED" "WIDOWED" ...
 $ race                : chr [1:431231] "WHITE" "WHITE" "WHITE" "WHITE" ...
 $ edregtime           : POSIXct[1:431231], format: "2180-05-06 19:17:00" "2180-06-26 15:54:00" ...
 $ edouttime           : POSIXct[1:431231], format: "2180-05-06 23:30:00" "2180-06-26 21:31:00" ...
 $ hospital_expire_flag: num [1:431231] 0 0 0 0 0 0 0 0 0 0 ...
 - attr(*, "spec")=
  .. cols(
  ..   subject_id = col_double(),
  ..   hadm_id = col_double(),
  ..   admittime = col_datetime(format = ""),
  ..   dischtime = col_datetime(format = ""),
  ..   deathtime = col_datetime(format = ""),
  ..   admission_type = col_character(),
  ..   admit_provider_id = col_character(),
  ..   admission_location = col_character(),
  ..   discharge_location = col_character(),
  ..   insurance = col_character(),
  ..   language = col_character(),
  ..   marital_status = col_character(),
  ..   race = col_character(),
  ..   edregtime = col_datetime(format = ""),
  ..   edouttime = col_datetime(format = ""),
  ..   hospital_expire_flag = col_double()
  .. )
 - attr(*, "problems")=<externalptr> 

In terms of the speed and based on the user time, fread is the fastest, followed by read_csv, and read.csv is the slowest.

In terms of the memory usage of the resultant dataframe or tibble, fread uses the least memory, followed by read_csv, and read.csv uses the most memory.

In terms of the default parsed data types, fread and read_csv are similar if “double” and “integer” are categorized as “numeric” data type; However, read.csv is different in some variables’ data type.

Q1.2 User-supplied data types

Re-ingest admissions.csv.gz by indicating appropriate column data types in read_csv. Does the run time change? How much memory does the result tibble use? (Hint: col_types argument in read_csv.)

Answer

system.time(data_read_csv <- read_csv(str_c(mimic_path,"admissions.csv.gz"), 
                                      col_types = "nnTTTccccccccTTn"))
   user  system elapsed 
  1.367   0.093   0.718 
pryr::object_size(data_read_csv)
55.31 MB

No. The run time almost does not change. The memory that the resultant tibble uses is 55.31 MB.

Q2. Ingest big data files

Let us focus on a bigger file, labevents.csv.gz, which is about 125x bigger than admissions.csv.gz.

ls -l ~/mimic/hosp/labevents.csv.gz

Display the first 10 lines of this file.

zcat < ~/mimic/hosp/labevents.csv.gz | head -10

Q2.1 Ingest labevents.csv.gz by read_csv

Try to ingest labevents.csv.gz using read_csv. What happens? If it takes more than 5 minutes on your computer, then abort the program and report your findings.

Q2.2 Ingest selected columns of labevents.csv.gz by read_csv

Try to ingest only columns subject_id, itemid, charttime, and valuenum in labevents.csv.gz using read_csv. Does this solve the ingestion issue? (Hint: col_select argument in read_csv.)

Q2.3 Ingest subset of labevents.csv.gz

Our first strategy to handle this big data file is to make a subset of the labevents data. Read the MIMIC documentation for the content in data file labevents.csv.

In later exercises, we will only be interested in the following lab items: creatinine (50912), potassium (50971), sodium (50983), chloride (50902), bicarbonate (50882), hematocrit (51221), white blood cell count (51301), and glucose (50931) and the following columns: subject_id, itemid, charttime, valuenum. Write a Bash command to extract these columns and rows from labevents.csv.gz and save the result to a new file labevents_filtered.csv.gz in the current working directory. (Hint: use zcat < to pipe the output of labevents.csv.gz to awk and then to gzip to compress the output. To save render time, put #| eval: false at the beginning of this code chunk.)

Display the first 10 lines of the new file labevents_filtered.csv.gz. How many lines are in this new file? How long does it take read_csv to ingest labevents_filtered.csv.gz?

Q2.4 Ingest labevents.csv by Apache Arrow

Our second strategy is to use Apache Arrow for larger-than-memory data analytics. Unfortunately Arrow does not work with gz files directly. First decompress labevents.csv.gz to labevents.csv and put it in the current working directory. To save render time, put #| eval: false at the beginning of this code chunk.

Then use arrow::open_dataset to ingest labevents.csv, select columns, and filter itemid as in Q2.3. How long does the ingest+select+filter process take? Display the number of rows and the first 10 rows of the result tibble, and make sure they match those in Q2.3. (Hint: use dplyr verbs for selecting columns and filtering rows.)

Write a few sentences to explain what is Apache Arrow. Imagine you want to explain it to a layman in an elevator.

Q2.5 Compress labevents.csv to Parquet format and ingest/select/filter

Re-write the csv file labevents.csv in the binary Parquet format (Hint: arrow::write_dataset.) How large is the Parquet file(s)? How long does the ingest+select+filter process of the Parquet file(s) take? Display the number of rows and the first 10 rows of the result tibble and make sure they match those in Q2.3. (Hint: use dplyr verbs for selecting columns and filtering rows.)

Write a few sentences to explain what is the Parquet format. Imagine you want to explain it to a layman in an elevator.

Q2.6 DuckDB

Ingest the Parquet file, convert it to a DuckDB table by arrow::to_duckdb, select columns, and filter rows as in Q2.5. How long does the ingest+convert+select+filter process take? Display the number of rows and the first 10 rows of the result tibble and make sure they match those in Q2.3. (Hint: use dplyr verbs for selecting columns and filtering rows.)

Write a few sentences to explain what is DuckDB. Imagine you want to explain it to a layman in an elevator.

Q3. Ingest and filter chartevents.csv.gz

chartevents.csv.gz contains all the charted data available for a patient. During their ICU stay, the primary repository of a patient’s information is their electronic chart. The itemid variable indicates a single measurement type in the database. The value variable is the value measured for itemid. The first 10 lines of chartevents.csv.gz are

zcat < ~/mimic/icu/chartevents.csv.gz | head -10

d_items.csv.gz is the dictionary for the itemid in chartevents.csv.gz.

zcat < ~/mimic/icu/d_items.csv.gz | head -10

In later exercises, we are interested in the vitals for ICU patients: heart rate (220045), mean non-invasive blood pressure (220181), systolic non-invasive blood pressure (220179), body temperature in Fahrenheit (223761), and respiratory rate (220210). Retrieve a subset of chartevents.csv.gz only containing these items, using the favorite method you learnt in Q2.

Document the steps and show code. Display the number of rows and the first 10 rows of the result tibble.